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Polypharmacy among Underserved Older African American Adults.

Mohsen BazarganJames SmithMasoud MovassaghiDavid MartinsHamed YazdanshenasSeyede Salehe MortazaviGail Orum
Published in: Journal of aging research (2017)
The purpose of the present study was to examine correlates of polypharmacy among underserved community-dwelling older African American adults. Methods. This study recruited 400 underserved older African Americans adults living in South Los Angeles. The structured face-to-face interviews collected data on participants' characteristics and elicited data pertaining to the type, frequency, dosage, and indications of all medications used by participants. Results. Seventy-five and thirty percent of participants take at least five and ten medications per day, respectively. Thirty-eight percent of participants received prescription medications from at least three providers. Inappropriate drug use occurred among seventy percent of the participants. Multivariate analysis showed that number of providers was the strongest correlate of polypharmacy. Moreover, data show that gender, comorbidity, and potentially inappropriate medication use are other major correlates of polypharmacy. Conclusions. This study shows a high rate of polypharmacy and potentially inappropriate medication use among underserved older African American adults. We documented strong associations between polypharmacy and use of potentially inappropriate medications, comorbidities, and having multiple providers. Polypharmacy and potentially inappropriate medications may be attributed to poor coordination and management of medications among providers and pharmacists. There is an urgent need to develop innovative and effective strategies to reduce inappropriate polypharmacy and potentially inappropriate medication in underserved elderly minority populations.
Keyphrases
  • african american
  • community dwelling
  • adverse drug
  • middle aged
  • electronic health record
  • physical activity
  • healthcare
  • big data
  • emergency department
  • machine learning
  • mental health
  • data analysis
  • drug induced